CN112036385B - Library position correction method and device, electronic equipment and readable storage medium - Google Patents
Library position correction method and device, electronic equipment and readable storage medium Download PDFInfo
- Publication number
- CN112036385B CN112036385B CN202011215755.5A CN202011215755A CN112036385B CN 112036385 B CN112036385 B CN 112036385B CN 202011215755 A CN202011215755 A CN 202011215755A CN 112036385 B CN112036385 B CN 112036385B
- Authority
- CN
- China
- Prior art keywords
- library
- bit line
- library bit
- corner
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
- G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
- G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2015/932—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles for parking operations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
- G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2015/932—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles for parking operations
- G01S2015/933—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles for parking operations for measuring the dimensions of the parking space when driving past
- G01S2015/934—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles for parking operations for measuring the dimensions of the parking space when driving past for measuring the depth, i.e. width, not length, of the parking space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20164—Salient point detection; Corner detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Multimedia (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Computer Networks & Wireless Communication (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a library position correction method, a library position correction device, electronic equipment and a readable storage medium, which can quickly find a library position near a vehicle in a complex environment, correct a library bit line and an angular point from coarse to fine, obviously improve the positioning precision of the library position, obtain a library position with smaller error and further assist the accurate parking of a parking function. The invention has the advantages of simple required hardware structure, modular function, high running speed, low resource consumption and high robustness.
Description
Technical Field
The present invention relates to the field of intelligent driving technologies, and in particular, to a method and an apparatus for correcting a parking space, an electronic device, and a readable storage medium.
Background
The visual search storehouse space function based on the vehicle-mounted 360-degree panoramic camera is taken as a substitute of the existing ultrasonic automatic parking function, and the visual search storehouse space function gradually steps into a mass production track.
The visual search library position function has the characteristics of high running speed, long detection distance and strong environment adaptability, and is low in cost and simple to install. However, due to the complex automatic parking scene, the uneven quality of the parking space lines, the various conditions of pollution, shielding, blurring, low contrast and the like, the visual parking space searching function has different degrees of detection angle deviation and size deviation, and thus the automatic parking function may be affected and even the parking fails.
Therefore, how to solve the problem of detection deviation of the visual search library position function in different scenes to ensure the stability, accuracy and safety of the visual automatic parking function is a problem that needs to be solved urgently at the present stage.
Disclosure of Invention
In view of the above, to solve the above problems, the present invention provides a method, an apparatus, an electronic device and a readable storage medium for correcting a library position, and the technical solution is as follows:
a method of library correction, the method comprising:
acquiring a bird-eye view of a vehicle, and identifying a storage area where a storage location is located in the bird-eye view;
determining an angular point distribution area by roughly positioning angular points in the library location area;
determining a connected region of the corner point distribution region, and detecting a library bit line mask of a sub-image corresponding to the connected region in the aerial view, wherein the library bit line mask is used for representing probability distribution of each pixel point in the sub-image belonging to a library bit line;
and determining the library bit lines of the library bit based on the library bit line mask, and taking the intersection points between the library bit lines of the library bit as the angular points of the library bit.
Preferably, the determining the corner distribution region by coarsely positioning the corners in the library location region includes:
calling a pre-trained corner recognition model and a limiting distance corresponding to a recognition error of the corner recognition model, wherein the corner recognition model is obtained by deep learning-based positioning regression algorithm training;
identifying the angular points in the library bit region through the angular point identification model;
and determining a corresponding corner distribution area according to the positions of the corners identified by the corner identification model and the limiting distance.
Preferably, the detecting a library bit line mask of the sub-image corresponding to the connected region in the bird's eye view includes:
respectively processing the connected regions by adopting a deep learning image segmentation method and an image processing and detecting boundary contour method to obtain corresponding library bit line classification results;
and weighting and fusing the library bit line classification result of the deep learning image segmentation method and the library bit line classification result of the image processing and boundary contour detection method to obtain a library bit line mask.
Preferably, before determining the library bit line of the library bit based on the library bit line mask, the method further comprises:
and correcting the library bit line mask by adopting a random condition field method.
A library correction device, the device comprising:
the system comprises a storage position identification module, a storage position identification module and a storage position identification module, wherein the storage position identification module is used for acquiring a bird's-eye view of a vehicle and identifying a storage position area where a storage position is located in the bird's-eye view;
the corner coarse positioning module is used for determining a corner distribution area by coarsely positioning corners in the library location area;
the mask detection module is used for determining a connected region of the corner point distribution region and detecting a library bit line mask of a sub-image corresponding to the connected region in the aerial view, wherein the library bit line mask is used for representing probability distribution of each pixel point in the sub-image belonging to a library bit line;
and the library position determining module is used for determining the library position line of the library position based on the library position line mask and taking the intersection point between the library position lines of the library position as the angular point of the library position.
Preferably, the corner point coarse positioning module is specifically configured to:
calling a pre-trained corner recognition model and a limiting distance corresponding to a recognition error of the corner recognition model, wherein the corner recognition model is obtained by deep learning-based positioning regression algorithm training; identifying the angular points in the library bit region through the angular point identification model; and determining a corresponding corner distribution area according to the positions of the corners identified by the corner identification model and the limiting distance.
Preferably, the mask detection module is specifically configured to:
respectively processing the connected regions by adopting a deep learning image segmentation method and an image processing and detecting boundary contour method to obtain corresponding library bit line classification results; and weighting and fusing the library bit line classification result of the deep learning image segmentation method and the library bit line classification result of the image processing and boundary contour detection method to obtain a library bit line mask.
Preferably, the mask detection module is further configured to:
and correcting the library bit line mask by adopting a random condition field method.
An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions when executed by the processor implementing the steps of any of the library bit correction methods.
A readable storage medium on which a program or instructions are stored, which when executed by a processor implement the steps of any of the library bit correction methods.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a library position correction method, a library position correction device, electronic equipment and a readable storage medium, which can quickly find a library position near a vehicle in a complex environment, correct a library bit line and an angular point from coarse to fine, obviously improve the positioning precision of the library position, obtain a library position with smaller error and further assist the accurate parking of a parking function. The invention has the advantages of simple required hardware structure, modular function, high running speed, low resource consumption and high robustness.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a block diagram of a hardware structure of an electronic device according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for library bit correction according to an embodiment of the present invention;
FIG. 3 is an exemplary image effect of a bird's eye view provided by an embodiment of the present invention;
FIG. 4 is an example of an image coordinate system of a bird's eye view provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a processing result of the bird's eye view provided by the embodiment of the present invention;
fig. 6 is a schematic structural diagram of a library position correction apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The main reservoir position detection methods for mass production in the market at present are all based on ultrasonic radar, and the method can only detect a space reservoir position, namely the condition that one empty reservoir position is formed between two parked vehicles. However, the ultrasonic radar has a short detection range, long response time and high scene requirement, so that the scheme has a plurality of problems:
the ultrasonic radar can detect the vehicle only when the vehicle passes through the garage; vehicles must be parked on two sides, otherwise, the storage position cannot be detected; the distance between the car and the garage is close, the driving speed is slow, and no obvious interference exists nearby.
This solution can only be used in parking lots with few pedestrians and vehicles, and is basically equivalent to failure in roads with dense pedestrian flow and complex traffic. Therefore, a visual method is required to quickly and accurately position the library.
Some solutions for visually detecting the library positions are available in the market at present, but the idea of the solutions is to perform image preprocessing first, then use a deep learning model to position regression, and regularize corner points of the found library positions, i.e. generate the detected library positions. Although this method is simple and efficient and fast, it has the problem that false detection is serious and similar images are often taken as a library. And the method has inaccurate positioning, which can cause parking deviation. This is the consequence of no correction scheme.
The invention aims to quickly, efficiently and accurately correct the edges and the details of the parking space by adopting a visual method, thereby providing a reliable guarantee for the first-step path planning for the automatic parking function. The error and speed of detecting the library position are required in the project, and the general deviation is required to be within 5 percent or even smaller. It is necessary to adopt a correction method.
Based on this, an embodiment of the present invention provides a library bit correction method, which is applied to an electronic device, and referring to a hardware structure block diagram of the electronic device shown in fig. 1, a hardware structure of the electronic device may include: a processor 11, a communication interface 12, a memory 13 and a communication bus 14;
in the embodiment of the present application, the number of the processor 11, the communication interface 12, the memory 13 and the communication bus 14 is at least one, and the processor 11, the communication interface 12 and the memory 13 are communicated with each other through the communication bus 14.
The processor 11 may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or an application Specific Integrated circuit (asic), or one or more Integrated circuits configured to implement embodiments of the present invention, etc.
The memory 13 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory) or the like, such as at least one disk memory.
Wherein, the memory 13 stores programs or instructions, and the processor 11 can call and execute the programs or instructions stored in the memory, and when the programs or instructions are executed by the processor 11, the programs or instructions can implement:
acquiring a bird-eye view of a vehicle, and identifying a storage area where a storage location is located in the bird-eye view; determining an angular point distribution area by roughly positioning angular points in the library location area; determining a connected region of the corner point distribution region, and detecting a library bit line mask of a sub-image corresponding to the connected region in the aerial view, wherein the library bit line mask is used for representing probability distribution of each pixel point in the sub-image belonging to a library bit line; and determining the library bit lines of the library bit based on the library bit line mask, and taking the intersection points between the library bit lines of the library bit as the angular points of the library bit.
It should be noted that the detailed functions and the extended functions of the program can be referred to the following description.
Referring to a method flowchart of the library location correction method shown in fig. 2, the library location correction method provided by the embodiment of the present invention includes the following steps:
and S10, acquiring the aerial view of the vehicle, and identifying the storage area where the storage is located in the aerial view.
In the embodiment of the invention, a vehicle-mounted 360-degree panoramic camera is arranged in a vehicle, based on the fact that the vehicle-mounted 360-degree panoramic camera can obtain a picture of the environment where the vehicle is located, the picture of the vehicle-mounted 360-degree panoramic camera is subjected to distortion correction, and a vehicle coordinate system is converted into an image coordinate system of a bird's-eye view, so that bird's-eye view mapping is completed, images are further spliced, a view field range with the length of each side of the vehicle body being not less than 6 meters is formed, and the image effect is shown in fig. 3.
In addition, the range of each side of the vehicle body not lower by 6 meters means that the front side, the rear side, the left side and the right side of the vehicle have the picture range of more than 6 meters, and the setting is to ensure that vertical, parallel and oblique storage positions can be completely seen in the picture. Generally, the depths of the vertical library position and the oblique library position are about 5 meters, and the width of the parallel library position is 5.6-6 and 0 meter, so that all kinds of library position types can be ensured to be seen in a picture.
See figure 4 for an example of an image coordinate system of a bird's eye view. The origin (0, 0) of the image coordinate system is at the upper left, the vehicle center coordinate is (500,325), and the other distance information is shown as a graph and has a unit of pixel. It is assumed that each pixel represents an actual distance of 2 cm, so that 400 pixels represent a distance range of 800cm to the left of the vehicle. Similarly, when we detect the library space on the image, the conversion can be performed by a scale of 1 pixel =2 cm, so as to realize the conversion from the world coordinate system to the image coordinate system of the bird's eye view.
In addition, in the embodiment of the present invention, a single-stage detector is used to detect the region of interest of the library location within the field of view, that is, the library location region, and the result is the position of the circumscribed rectangular frame of the region where each library location is located in the bird's eye view, as shown by the large rectangular frame 1 in the bird's eye view processing result shown in fig. 5.
And S20, determining a corner distribution area by roughly positioning corners in the library area.
In the embodiment of the invention, the angular points in the library bit region are identified through an intelligent learning algorithm so as to determine the positions of the angular points in the library bit region, and further, the positions can be used as centers to divide a certain region for the identified angular points to be used as angular point distribution regions of the angular points.
In a specific implementation process, in the embodiment of the present invention, the library location region is processed by using a deep learning localization regression algorithm, and in step S20, "determine the corner distribution region by roughly locating the corners in the library location region" may adopt the following steps:
calling a pre-trained corner recognition model and a limiting distance corresponding to a recognition error of the corner recognition model, wherein the corner recognition model is obtained by deep learning-based positioning regression algorithm training; identifying the angular points in the library bit region through an angular point identification model; and determining a corresponding corner distribution area according to the positions of the corners identified by the corner identification model and the limiting distance.
In the embodiment of the invention, a corner point identification model is trained based on a positioning regression algorithm, specifically, aiming at the library detection task, training samples are collected and labeled with a true value, a prediction result of a basic model on the trained samples approaches to the labeled true value, and the basic model is trained to obtain the corner point identification model with the performance meeting the requirements.
Of course, the existing algorithm structure needs to be modified appropriately to achieve optimal performance when necessary. The embodiment of the present invention is not limited thereto.
After the training of the corner point identification model is finished, the corner point identification model can be tested by using a test sample which does not participate in the training, and the identification error of the corner point identification model is calculated based on the test result and the true value marked by the test sample. Considering that the actual scene may be more complex, the restrictive distance may be set to be suitably larger than the recognition error of the corner recognition model.
Assuming that the identification error of the corner point identification model is 3 pixels, considering that the actual scene may be more complicated, the restrictive distance may be set to 4 pixels, that is, the position of the corner point is used as the center, and the upper, lower, left and right sides are respectively expanded by 4 pixels, that is, the error range of each corner point after the coarse positioning is (4 + 1 + 4) = 4 pixels. The corner point distribution areas obtained by the coarse positioning are shown by rectangular frames 2, 3, 4 and 5 in the bird's eye view processing result shown in fig. 5.
And S30, determining a connected region of the corner point distribution region, and detecting a library bit line mask of the sub-image corresponding to the connected region in the aerial view, wherein the library bit line mask is used for representing the probability distribution of each pixel point in the sub-image belonging to the library bit line.
In the embodiment of the invention, for the four identified corner points of the library, the corner point distribution areas of the four corner points can be obtained through the steps S10-S20, the four corner point distribution areas are communicated to form a complete communicated area, and images of the library area which do not belong to a non-communicated area are removed. The connected areas are shown as the dashed areas 6, 7, 8, 9 in the bird's eye view processing result shown in fig. 5.
In addition, any one of a depth learning image segmentation method and an image processing boundary contour detection method may be employed for the detection of the library bit line mask. The following describes the detection processes of the deep learning image segmentation method and the image processing boundary contour detection method respectively:
the deep learning image segmentation method comprises the following steps: for the subimages corresponding to the connected region, carrying out secondary classification on each pixel point by using deep learning; if the classification result of a certain pixel point is 0, the pixel point is not a point on the bit line of the library; if the classification result of a certain pixel point is 1, the pixel point is a point on the library bit line. Thus, the final result is all the pixels in the connected region that may constitute the bit line of the library.
The method for detecting the boundary contour by image processing comprises the following steps: for the sub-image corresponding to the connected region, the sub-image is converted into a gray scale map, and then the edge contour (i.e. the library bit line) in the image is drawn by using the canny algorithm. Therefore, the obtained results are all the pixel points forming the library bit line in the communication area, wherein the classification result of the pixel points belonging to the library bit line is 1, and the classification result of the pixel points not belonging to the library bit line is 0.
It should be noted that the above classification results "0" and "1" are probabilities that the pixel points belong to the library bit line, and thus the probability distribution of all the pixel points in the sub-image is the library bit line mask.
In a specific implementation process, in order to improve the detection accuracy of the library bit line mask, in step S30, "detecting the library bit line mask of the sub-image corresponding to the connected region" in the bird' S-eye view image may include the following steps:
respectively processing the connected regions by adopting a deep learning image segmentation method and an image processing and detecting boundary contour method to obtain corresponding library bit line classification results; and weighting and fusing the library bit line classification result of the depth learning image segmentation method and the library bit line classification result of the image processing and boundary contour detection method to obtain a library bit line mask.
In the embodiment of the invention, the weights of the deep learning image segmentation method and the image processing boundary contour detection method can be preset, and then the library bit line classification results of the deep learning image segmentation method and the image processing boundary contour detection method are weighted and fused based on the weights of the deep learning image segmentation method and the image processing boundary contour detection method to form the high-precision library bit line mask.
Based on the library bit line mask, a collection of pixels belonging to the library bit line can be obtained, for example, the pixels whose classification result value after weighted fusion is greater than a preset threshold are taken as the pixels belonging to the library bit line.
The following description will be given by taking an example in which the weight of the deep learning image segmentation method is 0.6 and the weight of the image processing boundary contour detection method is 0.4:
and for each pixel point in the sub-image, multiplying the classification result of the pixel point in the deep learning image segmentation method by 0.6, multiplying the classification result of the pixel point in the image processing boundary contour detection method by 0.4, and overlapping the results to obtain the actual classification result of the pixel point. The closer the actual classification result is to 1, the greater the likelihood that the pixel belongs to the library bit line.
In addition, because the boundary of the library bit line cannot be completely determined from the library bit line mask, the library bit line mask is corrected by adopting a random condition field method, so that the probability distribution of the library bit line mask is adjusted, the numerical value of the classification result close to the library bit line is higher, and the numerical value of the classification result not close to the library bit line is lower, and the boundary of the library bit line is clearly depicted.
Specifically, the random condition field is applied to the correction of the library bit line mask, and the main implementation mode is as follows:
and representing the library bit line mask by using an undirected graph, wherein each pixel point is a vertex in the undirected graph, and the connection relation among the pixel points is the connection line of the vertices. The process of image segmentation is to assign each vertex a different label (object or background), i.e. to correctly segment the edges in the undirected graph at the boundaries. Two pixels with similar position and color characteristics have a high probability of being assigned the same label, and are less likely to be segmented, which forms a probabilistic model of the stochastic conditional field.
That is, pixels near the bin bit line are merged if the location and color characteristics are similar. After each pixel point is processed and is endowed with label, the conditional random field carries out global normalization on all labels and features, and then an optimal solution is obtained. And accurately acquiring a pixel point collection belonging to the bit line of the library through the obtained new label distribution.
Furthermore, the collection of the pixel points belonging to the library bit line is taken as a boundary to be extracted, so that the inner boundary and/or the outer boundary of the library bit line can be obtained, and an angular point can be formed at the intersection of every two inner boundaries and/or outer boundaries. Therefore, the correction of the library bit line and the corner point can be completed, and the accurate positioning of the library bit in the automatic parking function is realized.
And S40, determining the library bit lines of the library bit based on the library bit line mask, and taking the intersection points between the library bit lines of the library bit as corner points of the library bit.
In summary, with the library bit correction method of the present invention, firstly, the effective connected region is quickly locked by using the deep learning single-stage detection method and the regression positioning algorithm, and the influence of the scene environment on the library bit line is removed to the greatest extent.
Further, the library bit line mask is processed in the connected region by adopting a deep learning image segmentation method, most of library bit lines can be extracted efficiently, but the extraction is not accurate enough. Meanwhile, the boundary contour is detected by adopting an image processing method, so that the library bit line can be more accurately determined. The advantages of the two methods are complementary, and the library bit line mask with stronger robustness can be obtained.
And moreover, the library bit line mask is reprocessed by using a random condition field method, so that the library bit line mask, image pixel gradient, unsupervised learning, image foreground and background distribution and other factors can be comprehensively considered and optimized, and the processed library bit line mask is ensured to be more consistent with the library bit line in a real scene.
The inner edge and the outer edge of the library bit line mask respectively correspond to the inner boundary and the outer boundary of the library bit line, so that the automatic parking function decision can be facilitated, and the used area can be freely selected. And the boundary intersection points are corner points of the library positions, so that the calculation is flexible and the use is convenient.
Based on the library location correction method provided in the above embodiments, an embodiment of the present invention further provides a device for executing the library location correction method, where a schematic structural diagram of the device is shown in fig. 6, and the device includes:
the storage location identification module 10 is used for acquiring a bird's-eye view of the vehicle and identifying a storage location area where the storage location is located in the bird's-eye view;
the corner coarse positioning module 20 is configured to determine a corner distribution area by coarsely positioning corners in the library area;
the mask detection module 30 is configured to determine a connected region of the corner distribution region, and detect a library bit line mask of a sub-image corresponding to the connected region in the bird's eye view, where the library bit line mask is used to represent probability distribution of each pixel point in the sub-image belonging to a library bit line;
and the library position determining module 40 is used for determining library position lines of the library positions based on the library position line mask, and taking intersection points among the library position lines of the library positions as angular points of the library positions.
Optionally, the corner point coarse positioning module 20 is specifically configured to:
calling a pre-trained corner recognition model and a limiting distance corresponding to a recognition error of the corner recognition model, wherein the corner recognition model is obtained by deep learning-based positioning regression algorithm training; identifying the angular points in the library bit region through an angular point identification model; and determining a corresponding corner distribution area according to the positions of the corners identified by the corner identification model and the limiting distance.
Optionally, the mask detection module 30 is specifically configured to:
respectively processing the connected regions by adopting a deep learning image segmentation method and an image processing and detecting boundary contour method to obtain corresponding library bit line classification results; and weighting and fusing the library bit line classification result of the depth learning image segmentation method and the library bit line classification result of the image processing and boundary contour detection method to obtain a library bit line mask.
Optionally, the mask detecting module 30 is further configured to:
the library bit line mask is modified using a random conditional field method.
The embodiment of the invention provides a library position correcting device which can quickly find a library position near a vehicle in a complex environment, correct a library bit line and an angular point from coarse to fine, obviously improve the positioning precision of the library position, and obtain a library position with smaller error, thereby assisting the accurate parking function.
Embodiments of the present application also provide a readable storage medium, on which a program or instructions are stored, and when executed by a processor, the program or the instructions implement the steps of the library bit correction method described above. The detailed function and the extended function of the program or the instructions may refer to the above description.
The library bit correction method, the library bit correction device, the electronic device and the readable storage medium provided by the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation of the invention, and the description of the above embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include or include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method of library correction, the method comprising:
acquiring a bird-eye view of a vehicle, and identifying a storage area where a storage location is located in the bird-eye view;
determining an angular point distribution area by roughly positioning angular points in the library location area;
determining a connected region of the corner point distribution region, and detecting a library bit line mask of a sub-image corresponding to the connected region in the aerial view, wherein the library bit line mask is used for representing probability distribution of each pixel point in the sub-image belonging to a library bit line;
determining the library bit lines of the library bit based on the library bit line mask, and taking the intersection points between the library bit lines of the library bit as the angular points of the library bit;
wherein the detecting a library bit line mask of the sub-image corresponding to the connected region in the aerial view comprises:
respectively processing the connected regions by adopting a deep learning image segmentation method and an image processing and detecting boundary contour method to obtain corresponding library bit line classification results;
and weighting and fusing the library bit line classification result of the deep learning image segmentation method and the library bit line classification result of the image processing and boundary contour detection method to obtain a library bit line mask.
2. The method of claim 1, wherein determining a corner distribution region by coarsely locating corners in the bin region comprises:
calling a pre-trained corner recognition model and a limiting distance corresponding to a recognition error of the corner recognition model, wherein the corner recognition model is obtained by deep learning-based positioning regression algorithm training;
identifying the angular points in the library bit region through the angular point identification model;
and determining a corresponding corner distribution area according to the positions of the corners identified by the corner identification model and the limiting distance.
3. The method of claim 1, wherein prior to said determining a library bit line of said library bit based on said library bit line mask, said method further comprises:
and correcting the library bit line mask by adopting a random condition field method.
4. An apparatus for correction of a library position, the apparatus comprising:
the system comprises a storage position identification module, a storage position identification module and a storage position identification module, wherein the storage position identification module is used for acquiring a bird's-eye view of a vehicle and identifying a storage position area where a storage position is located in the bird's-eye view;
the corner coarse positioning module is used for determining a corner distribution area by coarsely positioning corners in the library location area;
the mask detection module is used for determining a connected region of the corner point distribution region and detecting a library bit line mask of a sub-image corresponding to the connected region in the aerial view, wherein the library bit line mask is used for representing probability distribution of each pixel point in the sub-image belonging to a library bit line;
a library position determining module, configured to determine a library position line of the library position based on the library position line mask, and use an intersection point between the library position lines of the library position as an angular point of the library position;
wherein, the mask detection module is specifically configured to:
respectively processing the connected regions by adopting a deep learning image segmentation method and an image processing and detecting boundary contour method to obtain corresponding library bit line classification results; and weighting and fusing the library bit line classification result of the deep learning image segmentation method and the library bit line classification result of the image processing and boundary contour detection method to obtain a library bit line mask.
5. The apparatus according to claim 4, wherein the corner point coarse positioning module is specifically configured to:
calling a pre-trained corner recognition model and a limiting distance corresponding to a recognition error of the corner recognition model, wherein the corner recognition model is obtained by deep learning-based positioning regression algorithm training; identifying the angular points in the library bit region through the angular point identification model; and determining a corresponding corner distribution area according to the positions of the corners identified by the corner identification model and the limiting distance.
6. The apparatus of claim 4, wherein the mask inspection module is further configured to:
and correcting the library bit line mask by adopting a random condition field method.
7. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions when executed by the processor implementing the steps of the library bit correction method of any one of claims 1-3.
8. A readable storage medium, on which a program or instructions are stored, which when executed by a processor implement the steps of the library bit correction method of any one of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011215755.5A CN112036385B (en) | 2020-11-04 | 2020-11-04 | Library position correction method and device, electronic equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011215755.5A CN112036385B (en) | 2020-11-04 | 2020-11-04 | Library position correction method and device, electronic equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112036385A CN112036385A (en) | 2020-12-04 |
| CN112036385B true CN112036385B (en) | 2021-02-02 |
Family
ID=73572883
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011215755.5A Active CN112036385B (en) | 2020-11-04 | 2020-11-04 | Library position correction method and device, electronic equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112036385B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112874510B (en) * | 2021-02-02 | 2022-09-16 | 江苏大学 | Method for detecting parking space in automatic parking through automatic encoder based on semi-supervised learning |
| CN116363631B (en) * | 2023-05-19 | 2023-09-05 | 小米汽车科技有限公司 | Three-dimensional target detection method and device and vehicle |
| CN116863429B (en) * | 2023-07-26 | 2024-05-31 | 小米汽车科技有限公司 | Training method of detection model, and determination method and device of exercisable area |
| CN117853579B (en) * | 2023-10-07 | 2024-08-02 | 湖州丽天智能科技有限公司 | Photovoltaic panel pose correction method, photovoltaic robot and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831595A (en) * | 2012-06-20 | 2012-12-19 | 中国农业大学 | Marker detection method for image recognition of target points in natural environments |
| CN105913685A (en) * | 2016-06-25 | 2016-08-31 | 上海大学 | Video surveillance-based carport recognition and intelligent guide method |
| CN107672588A (en) * | 2017-08-29 | 2018-02-09 | 广州小鹏汽车科技有限公司 | A kind of automatic parking path obstructions collision checking method, apparatus and system |
| CN107993263A (en) * | 2017-10-27 | 2018-05-04 | 深圳市易成自动驾驶技术有限公司 | Viewing system automatic calibration method, automobile, caliberating device and storage medium |
| EP3409565A1 (en) * | 2017-05-29 | 2018-12-05 | LG Electronics Inc. | Parking system for vehicle and vehicle |
| CN109435942A (en) * | 2018-10-31 | 2019-03-08 | 合肥工业大学 | A kind of parking stall line parking stall recognition methods and device based on information fusion |
| CN110232835A (en) * | 2019-06-27 | 2019-09-13 | 浙江工业大学 | A kind of underground garage parking space detection method based on image procossing |
| CN110758246A (en) * | 2018-07-25 | 2020-02-07 | 广州小鹏汽车科技有限公司 | Automatic parking method and device |
| CN111178295A (en) * | 2019-12-31 | 2020-05-19 | 华为技术有限公司 | Parking space detection and model training method and device, vehicle, equipment and storage medium |
| CN111508260A (en) * | 2019-01-30 | 2020-08-07 | 上海欧菲智能车联科技有限公司 | Vehicle parking space detection method, device and system |
-
2020
- 2020-11-04 CN CN202011215755.5A patent/CN112036385B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831595A (en) * | 2012-06-20 | 2012-12-19 | 中国农业大学 | Marker detection method for image recognition of target points in natural environments |
| CN105913685A (en) * | 2016-06-25 | 2016-08-31 | 上海大学 | Video surveillance-based carport recognition and intelligent guide method |
| EP3409565A1 (en) * | 2017-05-29 | 2018-12-05 | LG Electronics Inc. | Parking system for vehicle and vehicle |
| CN107672588A (en) * | 2017-08-29 | 2018-02-09 | 广州小鹏汽车科技有限公司 | A kind of automatic parking path obstructions collision checking method, apparatus and system |
| CN107993263A (en) * | 2017-10-27 | 2018-05-04 | 深圳市易成自动驾驶技术有限公司 | Viewing system automatic calibration method, automobile, caliberating device and storage medium |
| CN110758246A (en) * | 2018-07-25 | 2020-02-07 | 广州小鹏汽车科技有限公司 | Automatic parking method and device |
| CN109435942A (en) * | 2018-10-31 | 2019-03-08 | 合肥工业大学 | A kind of parking stall line parking stall recognition methods and device based on information fusion |
| CN111508260A (en) * | 2019-01-30 | 2020-08-07 | 上海欧菲智能车联科技有限公司 | Vehicle parking space detection method, device and system |
| CN110232835A (en) * | 2019-06-27 | 2019-09-13 | 浙江工业大学 | A kind of underground garage parking space detection method based on image procossing |
| CN111178295A (en) * | 2019-12-31 | 2020-05-19 | 华为技术有限公司 | Parking space detection and model training method and device, vehicle, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112036385A (en) | 2020-12-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112036385B (en) | Library position correction method and device, electronic equipment and readable storage medium | |
| US11455805B2 (en) | Method and apparatus for detecting parking space usage condition, electronic device, and storage medium | |
| Varadharajan et al. | Vision for road inspection | |
| US20180018528A1 (en) | Detecting method and device of obstacles based on disparity map and automobile driving assistance system | |
| Azimi et al. | Eagle: Large-scale vehicle detection dataset in real-world scenarios using aerial imagery | |
| CN112740225B (en) | Method and device for determining road surface elements | |
| CN111814593B (en) | Traffic scene analysis method and equipment and storage medium | |
| CN109977776A (en) | A kind of method for detecting lane lines, device and mobile unit | |
| CN112598922B (en) | Parking space detection method, device, equipment and storage medium | |
| CN111967396A (en) | Processing method, device and equipment for obstacle detection and storage medium | |
| CN115372990A (en) | High-precision semantic map building method and device and unmanned vehicle | |
| CN114663852A (en) | Method and device for constructing lane line graph, electronic equipment and readable storage medium | |
| CN111191482B (en) | Brake lamp identification method and device and electronic equipment | |
| CN114792416A (en) | Target detection method and device | |
| CN113255444A (en) | Training method of image recognition model, image recognition method and device | |
| CN116543143A (en) | Training method of target detection model, target detection method and device | |
| CN111881752B (en) | Guardrail detection classification method and device, electronic equipment and storage medium | |
| CN112052807A (en) | Vehicle position detection method, device, electronic equipment and storage medium | |
| CN112784817B (en) | Method, device and equipment for detecting lane where vehicle is located and storage medium | |
| CN113435350A (en) | Traffic marking detection method, device, equipment and medium | |
| Meuter et al. | 3D traffic sign tracking using a particle filter | |
| CN118230279A (en) | Traffic signal lamp identification method, equipment, medium and vehicle based on twin network | |
| CN112837404A (en) | Method and device for constructing three-dimensional information of planar object | |
| CN116721396A (en) | Lane line detection method, device and storage medium | |
| CN114419531A (en) | Object detection method, object detection system, and computer-readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221201 Address after: Room 901, No. 18, Tianshan Road, Changning District, Shanghai 200050 Patentee after: Shanghai Tongshi Electronic Technology Co.,Ltd. Address before: Room 2100-b1018, 2 Huatian Road, Huayuan Industrial Zone, Binhai New Area, Tianjin Patentee before: TIANJIN TIANTONG WEISHI ELECTRONIC TECHNOLOGY Co.,Ltd. |